AI Development Approaches

The more I engage with organizations navigating AI transformation, the more I’m seeing a number of “flavors” 🍦 of AI deployment. Amidst this variety, several patterns are emerging, from activating functionality of tools embedded in daily workflows to bespoke, large-scale systems transforming operations. Here are the common approaches I’m seeing: A) Small, Focused Add-On to Current Tools: Many teams start by experimenting with AI features embedded in familiar tools, often within a single team or department. This approach is quick, low-risk, and delivers measurable early wins. Example: A sales team uses Salesforce Einstein AI to identify high-potential leads and prioritize follow-ups effectively. B) Scaling Pre-Built Tools Across Functions: Some organizations roll out ready-made AI solutions across entire functions—like HR, marketing, or customer service—to tackle specific challenges. Example: An HR team adopts HireVue’s AI platform to screen resumes and shortlist candidates, reducing time-to-hire and improving consistency. C) Localized, Nimble AI Tools for Targeted Needs: Some teams deploy focused AI tools for specific tasks or localized needs. These are quick to adopt but can face challenges scaling. Example: A marketing team uses Jasper AI to rapidly generate campaign content, streamlining creative workflows. D) Collaborating with Technology Partners: Partnering with tech providers allows organizations to co-create tailored AI solutions for cross-functional challenges. Example: A global manufacturer collaborates with IBM Watson to predict equipment failures, minimizing costly downtime. E) Building Fully Custom, Organization-Wide AI Solutions: Some enterprises invest heavily in custom AI systems aligned with their unique strategies and needs. While resource-intensive, this approach offers unparalleled control and integration. Example: JPMorgan Chase develops proprietary AI systems for fraud detection and financial forecasting across global operations. F) Scaling External Tools Across the Enterprise: Organizations sometimes deploy external AI tools organization-wide, prioritizing consistency and ease of adoption. Example: ChatGPT Enterprise is integrated across an organization’s productivity suite, standardizing AI-powered efficiency gains. G) Enterprise-Wide AI Solutions Developed Through Partnerships: For systemic challenges, organizations collaborate with partners to design AI solutions spanning departments and regions. Example: Google Cloud AI works with healthcare networks to optimize diagnostics and treatment pathways across hospital systems. Which approaches resonate most with your organization’s journey? Or are you blending them into something uniquely yours? With so many ways for this technology to transform jobs, processes, and organizations, it’s important we get clear about what flavor we’re trying 🍨 so we know how to do it right. #AIAdoption #ChangeManagement #AIIntegration #Leadership

"In what follows, we first discuss the key technological trajectories that have shaped AI developments, from rule-based systems to modern generative AI, highlighting their distinctive characteristics and implications. We then shed light on the various modalities of learning in AI, examining how different learning approaches—from supervised learning to zero-shot capabilities— have expanded AI’s adaptability and applicability. Following this, we explore the fundamental concepts underlying AI systems, including the crucial roles of data, training, and inference in shaping AI capabilities. We then trace the evolution of AI model capabilities across multiple dimensions, from traditional machine learning through deep learning breakthroughs, to the emergence of generative abilities and multimodal integration. This is complemented by an examination of recent developments in code generation, automation, and the emergence of AI models and agents with reasoning capabilities. In the final sections, we examine the practical aspects of implementing AI systems. We begin by exploring the challenges and methodologies of model evaluation, which has become increasingly complex following the emergence of generative AI systems. We then turn to the evolution of hardware infrastructure, tracing how computational requirements have shaped AI development and deployment. Finally, we analyze the critical role of system design and user interfaces in translating AI capabilities into practical applications, highlighting how these elements mediate between AI models and end users." From UNESCO, thanks to Jared Browne for sharing.

How Does Artificial Intelligence Work? AI is revolutionizing industries across the globe, from healthcare to finance, retail, and beyond. But have you ever wondered how AI systems are actually built? Here’s a step-by-step breakdown of the AI development process: 1️⃣ Problem Definition – The first step is identifying the problem AI is supposed to solve. Clearly defining objectives and expected outcomes is crucial for success. 2️⃣ Data Collection & Preparation – AI models rely on high-quality data. This step involves gathering, cleaning, and annotating data, ensuring it is structured and split into training, validation, and testing datasets. 3️⃣ Model Selection & Algorithm Development – Choosing the right AI model and algorithm is vital. This stage involves selecting an appropriate architecture and fine-tuning hyperparameters for optimal performance. 4️⃣ Model Training – The AI model is trained using vast amounts of data. It learns by adjusting weights to minimize errors and improve accuracy. Monitoring training progress helps refine the model. 5️⃣ Model Evaluation – Testing the trained model on unseen data helps measure its accuracy, precision, recall, and other key performance metrics. Any gaps or weaknesses identified here guide further improvements. 6️⃣ Fine-Tuning & Optimization – This stage involves refining the model using advanced techniques such as regularization, hyperparameter tuning, and optimizing its generalization capabilities to improve performance. 7️⃣ Deployment – Once the AI model performs well, it is integrated into real-world applications. Continuous monitoring ensures it remains accurate and adapts to new data. 8️⃣ Ethical Considerations – AI must be fair, transparent, and secure. Addressing bias, ensuring accountability, and complying with privacy regulations are essential for responsible AI deployment. From concept to deployment, AI development is a continuous learning cycle. The process doesn’t end with implementation—ongoing monitoring, feedback loops, and ethical considerations ensure AI solutions stay effective and reliable. The future of AI is bright, and its potential is limitless! How do you see AI impacting your industry? Let’s discuss in the comments! ⬇️

 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 – 𝗨𝗻𝗯𝗼𝘅𝗲𝗱! Building AI products today is no longer just about plugging in a model—it's about orchestrating a full-stack system that is modular, scalable, and intelligent by design. This Enhanced AI Product Stack blueprint captures a holistic approach to AI system architecture, designed to serve enterprise-grade use cases across industries. 🔹 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗟𝗮𝘆𝗲𝗿  Your AI product is only as strong as the foundation it's built on. Compute power, high-throughput networking, secure storage, and accelerators (GPUs/TPUs) provide the muscle to run complex models efficiently. 🔹 𝗧𝗼𝗼𝗹𝘀 & 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗟𝗮𝘆𝗲𝗿  This layer bridges cloud infrastructure with intelligence. It connects major providers like Microsoft, Google, AWS, and AI-first platforms such as Hugging Face and OpenAI—enabling access to cutting-edge models and scalable APIs. 🔹 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗟𝗮𝘆𝗲𝗿  This is where things get truly intelligent. An interconnected ecosystem of agents—Orchestrator, Reasoning, Retrieval, Execution—communicate A2A (agent-to-agent) to perform autonomous decision-making.  Powered by LLMs, fine-tuned models, RAG systems, vector DBs, and GenAI Ops, this layer is the brain behind adaptive, context-aware systems. 🔹 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿  The user-facing layer that brings everything together. Whether it’s authentication, UI/UX, monitoring, or context handling—this is where product experience and intelligence meet. 🔍 What makes this architecture unique is its support for AG-UI and MCP protocols, enabling seamless data and control flows between applications, agents, and services. 💡 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀:  This isn’t just about deploying AI. It’s about creating autonomous systems that learn, reason, and evolve. Businesses that adopt this layered architecture will find themselves far ahead in innovation, adaptability, and scale. 𝗔𝘀 𝗔𝗜 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗲𝘀 𝘁𝗼 𝗲𝘃𝗼𝗹𝘃𝗲—𝗮𝗿𝗲 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲, 𝗼𝗿 𝗷𝘂𝘀𝘁 𝗿𝗲𝗮𝗰𝘁𝗶𝗻𝗴 𝘁𝗼 𝘁𝗵𝗲 𝗽𝗿𝗲𝘀𝗲𝗻𝘁? Follow Dr. Rishi Kumar for similar insights! ------- 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 - https://lnkd.in/dFtDWPi5 𝗫 - https://x.com/contactrishi 𝗠𝗲𝗱𝗶𝘂𝗺 - https://lnkd.in/d8_f25tH

Introducing the AI Enablement Stack: A Comprehensive Mapping of 100+ Companies Shaping the Future of AI Development I'm excited to share our open-source initiative mapping the complete ecosystem of AI development tools and platforms. Here's how leading companies are building the future across five critical layers: Infrastructure Layer: • AI Workspaces: Daytona, Runloop AI, E2B • Model Access: Mistral AI, Groq, AI21 Labs, Cohere, Hugging Face, Cartesia, Fireworks AI, Together AI • Cloud: Koyeb, Nebius Intelligence Layer: • Frameworks: LangChain, LlamaIndex, Pydantic • Knowledge Engines: Pinecone, Weaviate, Chroma, Milvus, Qdrant, Supabase • Specialized Models: Codestral , Claude, Qwen, poolside Malibu Engineering Layer: • Training: Lamini, Predibase, Modal, Lightning AI • Tools: Relevance AI, Greptile, Sourcegraph, PromptLayer • Testing: Weights & Biases Governance Layer: • Pipeline: Portkey AI, Baseten, Stack AI • Monitoring: Cleanlab, Patronus AI, Log10, Traceloop, WhyLabs • Security: LiteLLM (YC W23), Martian • Compliance: Lakera AI 🤖 Agent Consumer Layer: • Autonomous: Devin (Cognition), OpenHands, Lovable • Assistive: GitHub Copilot, Continue, Sourcegraph Cody, Cursor • Specialized: CodeRabbit, Qodo (formerly Codium), Ellipsis, Codeflash Why This Matters: The world is moving toward an agentic future where AI agents will become integral to software development. Understanding this stack is crucial for: • Technical leaders planning AI infrastructure • Developers choosing tools and frameworks • Startups identifying market opportunities • Enterprises building AI strategies Check the first reply for the full article link and GitHub repository where you can contribute to this living document. What companies would you add to this mapping? Let's make this a living document that grows with our rapidly evolving AI ecosystem.

Google Cloud Next: Key Insights for AI Devs 🚀 Just wrapped up an inspiring Google Cloud Next, and wanted to share the highlights that I think are particularly relevant for those of us building the future of AI. A major takeaway was the focus on infrastructure built for the next wave of AI. 👉The new TPU v7 "Ironwood" is a beast, offering the power and memory bandwidth needed for the increasingly complex models we're working with. This isn't just about training; it's about having the horsepower to continuously run sophisticated AI. What really stood out to me was Google's strong push into making agent development a reality. This shift is huge for how we'll be building AI going forward. Key elements for developers include: 🟢 Agent2Agent (A2A) Protocol: This shared language will be crucial for building systems where different AI agents can communicate and collaborate effectively across models and tools. 🟢 Vertex AI Agent Builder: This new tool looks incredibly promising for streamlining the process of creating agents with integrated tools, memory, and reasoning capabilities. 🟢 Gemini Code Assist: Having more powerful AI-powered copilots directly integrated into the development workflow will be a game-changer for productivity. It's clear that Vertex AI is evolving into a comprehensive platform designed specifically for building and deploying these intelligent agents – going beyond just model training. We're seeing a move towards thinking in terms of context management, tool orchestration, and understanding the long-term behavior of AI systems. Ultimately, the future of AI development is pointing towards building coordinated, persistent systems that can learn, plan, and interact with their environment in real-time. This means focusing on things like long-term memory, multi-step decision-making, and seamless integration with various tools and other agents. Link to a more detailed overview in the comments Richard Seroter Karl Weinmeister Jeff Dean Thomas Kurian Oriol Vinyals Ivan 🥁 Nardini (Another highlight from the week was @arizeAI being announced in the keynote!)

Choosing the right AI approach can be the game changer for product innovation. Whether you're harnessing Predictive AI for data-driven insights, leveraging Generative AI for creative content, or deploying Agentic AI to drive autonomous workflows, understanding the strengths and trade-offs of each is essential. I present a comprehensive guide for selecting the right AI paradigm, ensuring alignment with technical, business, and ethical considerations. By leveraging the comparison tables, decision frameworks, and case studies, AI Product Managers and AI Solutions Architects can make informed decisions, driving successful AI implementations. This article dives deep into AI capability-fit analysis—offering actionable frameworks, recent case studies, and a pragmatic guide for AI Product Managers and engineers looking to build cutting-edge solutions that balance cost, accuracy, and automation. Read on to unlock the blueprint for next-gen AI product innovation. #AI #AIProductInnovation #AICapability #PredictiveAI #GenerativeAI #AgenticAI #AIFuture

The New Agent Development Lifecycle—Building AI Like Software #SalesforcePartner Building AI agents isn’t like traditional software development. At TDX 2025 today, Alice Steinglass, EVP and GM, Salesforce Platform, demonstrated an insightful new approach that I want to share with you. When you build agents: ❌ You can’t just write deterministic logic. ❌ You can’t hardcode every possible scenario. ❌ You can’t assume AI will always behave the same way. That’s why Salesforce introduced the Agentforce Development Lifecycle. 🛠 Build: Define goals, use AI assistance, and connect real business data. 🧪 Test: AI is non-deterministic—you need stochastic testing, automated utterance validation, and continuous monitoring. 🚀 Deploy: Promote agents through secure pipelines and track their evolution. 🔍 Observe: Use real-time analytics, logs, and adaptive feedback to refine performance. This is how AI development matures—from isolated experiments to enterprise-grade, production-ready AI. The question is: Are you experimenting with AI or engineering it? #AI #AgenticAI #TDX25 #AIDevelopment